Overview

Dataset statistics

Number of variables20
Number of observations16376
Missing cells0
Missing cells (%)0.0%
Total size in memory2.5 MiB
Average record size in memory160.0 B

Variable types

Numeric13
Categorical7

Alerts

rec_online_8 has constant value "0.0" Constant
sum_recharge is highly correlated with recharge_frequencyHigh correlation
recharge_frequency is highly correlated with sum_recharge and 1 other fieldsHigh correlation
sos_rec_5 is highly correlated with recharge_frequencyHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
sum_recharge is highly correlated with recharge_frequency and 3 other fieldsHigh correlation
recharge_frequency is highly correlated with sum_recharge and 2 other fieldsHigh correlation
rec_online_10 is highly correlated with recharge_frequencyHigh correlation
rec_online_15 is highly correlated with sum_rechargeHigh correlation
sos_rec_5 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
rec_online_20_b2 is highly correlated with sum_rechargeHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
sum_recharge is highly correlated with recharge_frequencyHigh correlation
recharge_frequency is highly correlated with sum_rechargeHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
chip_pre_rec_20 is highly correlated with rec_online_8High correlation
rec_online_8 is highly correlated with chip_pre_rec_20 and 5 other fieldsHigh correlation
rec_online_100_b18 is highly correlated with rec_online_8High correlation
venda is highly correlated with rec_online_8High correlation
pct_rec_1190 is highly correlated with rec_online_8High correlation
sos_rec_3 is highly correlated with rec_online_8High correlation
chip_pre_rec_10 is highly correlated with rec_online_8High correlation
sum_recharge is highly correlated with recharge_frequency and 4 other fieldsHigh correlation
recharge_frequency is highly correlated with sum_recharge and 5 other fieldsHigh correlation
rec_online_10 is highly correlated with recharge_frequency and 1 other fieldsHigh correlation
rec_online_15 is highly correlated with sum_recharge and 2 other fieldsHigh correlation
sos_rec_5 is highly correlated with sum_recharge and 5 other fieldsHigh correlation
rec_online_20_b2 is highly correlated with sum_recharge and 2 other fieldsHigh correlation
rec_online_13 is highly correlated with recharge_frequency and 1 other fieldsHigh correlation
rec_online_50_b8 is highly correlated with sum_rechargeHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
pct_rec_sos_5 is highly skewed (γ1 = 58.86004794) Skewed
rec_online_10 has 8166 (49.9%) zeros Zeros
rec_online_35_b5 has 15660 (95.6%) zeros Zeros
rec_online_15 has 8988 (54.9%) zeros Zeros
sos_rec_5 has 8976 (54.8%) zeros Zeros
rec_online_20_b2 has 9534 (58.2%) zeros Zeros
rec_online_13 has 13874 (84.7%) zeros Zeros
rec_online_50_b8 has 16052 (98.0%) zeros Zeros
rec_online_30_b4 has 14848 (90.7%) zeros Zeros
rec_online_40_b6 has 15906 (97.1%) zeros Zeros
pct_rec_690 has 16256 (99.3%) zeros Zeros
pct_rec_sos_5 has 16359 (99.9%) zeros Zeros

Reproduction

Analysis started2022-03-22 22:36:44.172693
Analysis finished2022-03-22 22:37:01.172760
Duration17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

sum_recharge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct431
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.67569614
Minimum0
Maximum1133
Zeros56
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:01.238545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median60
Q3118
95-th percentile220
Maximum1133
Range1133
Interquartile range (IQR)88

Descriptive statistics

Standard deviation77.13705105
Coefficient of variation (CV)0.9330075784
Kurtosis14.28752919
Mean82.67569614
Median Absolute Deviation (MAD)40
Skewness2.564712715
Sum1353897.2
Variance5950.124644
MonotonicityNot monotonic
2022-03-22T19:37:01.319274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201124
 
6.9%
15906
 
5.5%
10841
 
5.1%
30756
 
4.6%
40710
 
4.3%
60625
 
3.8%
35511
 
3.1%
45497
 
3.0%
50472
 
2.9%
25459
 
2.8%
Other values (421)9475
57.9%
ValueCountFrequency (%)
056
 
0.3%
31
 
< 0.1%
5149
 
0.9%
6.926
 
0.2%
10841
5.1%
11.928
 
0.2%
13186
 
1.1%
13.84
 
< 0.1%
15906
5.5%
1854
 
0.3%
ValueCountFrequency (%)
11331
< 0.1%
11301
< 0.1%
10411
< 0.1%
10251
< 0.1%
8091
< 0.1%
8031
< 0.1%
7561
< 0.1%
7401
< 0.1%
7001
< 0.1%
6791
< 0.1%

recharge_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.333842208
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:01.411961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile18
Maximum111
Range110
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.238280663
Coefficient of variation (CV)0.9849125472
Kurtosis20.78759682
Mean6.333842208
Median Absolute Deviation (MAD)3
Skewness3.051570779
Sum103723
Variance38.91614563
MonotonicityNot monotonic
2022-03-22T19:37:01.489705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12574
15.7%
22323
14.2%
32053
12.5%
41553
9.5%
51142
 
7.0%
61033
 
6.3%
7791
 
4.8%
8688
 
4.2%
9605
 
3.7%
10540
 
3.3%
Other values (54)3074
18.8%
ValueCountFrequency (%)
12574
15.7%
22323
14.2%
32053
12.5%
41553
9.5%
51142
7.0%
61033
6.3%
7791
 
4.8%
8688
 
4.2%
9605
 
3.7%
10540
 
3.3%
ValueCountFrequency (%)
1111
 
< 0.1%
961
 
< 0.1%
891
 
< 0.1%
851
 
< 0.1%
691
 
< 0.1%
662
< 0.1%
641
 
< 0.1%
623
< 0.1%
611
 
< 0.1%
603
< 0.1%

rec_online_10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.913165608
Minimum0
Maximum33
Zeros8166
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:01.564452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.299766557
Coefficient of variation (CV)1.724767862
Kurtosis10.18656018
Mean1.913165608
Median Absolute Deviation (MAD)1
Skewness2.775503579
Sum31330
Variance10.88845933
MonotonicityNot monotonic
2022-03-22T19:37:01.633225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
08166
49.9%
12820
 
17.2%
21506
 
9.2%
3947
 
5.8%
4667
 
4.1%
5448
 
2.7%
6343
 
2.1%
7261
 
1.6%
8256
 
1.6%
9196
 
1.2%
Other values (23)766
 
4.7%
ValueCountFrequency (%)
08166
49.9%
12820
 
17.2%
21506
 
9.2%
3947
 
5.8%
4667
 
4.1%
5448
 
2.7%
6343
 
2.1%
7261
 
1.6%
8256
 
1.6%
9196
 
1.2%
ValueCountFrequency (%)
332
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
294
< 0.1%
282
< 0.1%
273
< 0.1%
262
< 0.1%
253
< 0.1%
242
< 0.1%
232
< 0.1%

rec_online_35_b5
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07804103566
Minimum0
Maximum9
Zeros15660
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:01.696015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4363809217
Coefficient of variation (CV)5.591685425
Kurtosis80.6200778
Mean0.07804103566
Median Absolute Deviation (MAD)0
Skewness7.805585488
Sum1278
Variance0.1904283088
MonotonicityNot monotonic
2022-03-22T19:37:01.751829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
015660
95.6%
1395
 
2.4%
2169
 
1.0%
3107
 
0.7%
420
 
0.1%
515
 
0.1%
65
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
015660
95.6%
1395
 
2.4%
2169
 
1.0%
3107
 
0.7%
420
 
0.1%
515
 
0.1%
65
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
73
 
< 0.1%
65
 
< 0.1%
515
 
0.1%
420
 
0.1%
3107
 
0.7%
2169
 
1.0%
1395
 
2.4%
015660
95.6%

rec_online_15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.295249145
Minimum0
Maximum36
Zeros8988
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:01.814615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum36
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.45697599
Coefficient of variation (CV)1.89691381
Kurtosis23.47162147
Mean1.295249145
Median Absolute Deviation (MAD)0
Skewness3.85060484
Sum21211
Variance6.036731016
MonotonicityNot monotonic
2022-03-22T19:37:01.884385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
08988
54.9%
13146
 
19.2%
21547
 
9.4%
3931
 
5.7%
4524
 
3.2%
5333
 
2.0%
6213
 
1.3%
7174
 
1.1%
8116
 
0.7%
990
 
0.5%
Other values (22)314
 
1.9%
ValueCountFrequency (%)
08988
54.9%
13146
 
19.2%
21547
 
9.4%
3931
 
5.7%
4524
 
3.2%
5333
 
2.0%
6213
 
1.3%
7174
 
1.1%
8116
 
0.7%
990
 
0.5%
ValueCountFrequency (%)
361
 
< 0.1%
351
 
< 0.1%
331
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
273
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
231
 
< 0.1%
222
< 0.1%

sos_rec_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.305263801
Minimum0
Maximum48
Zeros8976
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:01.959135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum48
Range48
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.427991804
Coefficient of variation (CV)1.860154095
Kurtosis37.48716218
Mean1.305263801
Median Absolute Deviation (MAD)0
Skewness4.34861299
Sum21375
Variance5.895144199
MonotonicityNot monotonic
2022-03-22T19:37:02.029899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
08976
54.8%
13012
 
18.4%
21505
 
9.2%
3989
 
6.0%
4559
 
3.4%
5433
 
2.6%
6260
 
1.6%
7184
 
1.1%
8137
 
0.8%
990
 
0.5%
Other values (24)231
 
1.4%
ValueCountFrequency (%)
08976
54.8%
13012
 
18.4%
21505
 
9.2%
3989
 
6.0%
4559
 
3.4%
5433
 
2.6%
6260
 
1.6%
7184
 
1.1%
8137
 
0.8%
990
 
0.5%
ValueCountFrequency (%)
481
 
< 0.1%
441
 
< 0.1%
421
 
< 0.1%
341
 
< 0.1%
292
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
243
< 0.1%

rec_online_20_b2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.071384954
Minimum0
Maximum29
Zeros9534
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:02.097674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.93357717
Coefficient of variation (CV)1.804745496
Kurtosis16.25557445
Mean1.071384954
Median Absolute Deviation (MAD)0
Skewness3.193877393
Sum17545
Variance3.738720671
MonotonicityNot monotonic
2022-03-22T19:37:02.161459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
09534
58.2%
12971
 
18.1%
21503
 
9.2%
3852
 
5.2%
4536
 
3.3%
5312
 
1.9%
6250
 
1.5%
7153
 
0.9%
881
 
0.5%
952
 
0.3%
Other values (13)132
 
0.8%
ValueCountFrequency (%)
09534
58.2%
12971
 
18.1%
21503
 
9.2%
3852
 
5.2%
4536
 
3.3%
5312
 
1.9%
6250
 
1.5%
7153
 
0.9%
881
 
0.5%
952
 
0.3%
ValueCountFrequency (%)
291
 
< 0.1%
261
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
192
 
< 0.1%
173
 
< 0.1%
164
 
< 0.1%
158
< 0.1%
147
< 0.1%
1317
0.1%

chip_pre_rec_10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
15709 
 
667

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
15709
95.9%
667
 
4.1%

Length

2022-03-22T19:37:02.229230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:02.272130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
15709
95.9%
667
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

chip_pre_rec_20
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
16128 
 
239
 
8
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Common Values

ValueCountFrequency (%)
16128
98.5%
239
 
1.5%
8
 
< 0.1%
1
 
< 0.1%

Length

2022-03-22T19:37:02.317936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:02.360789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16128
98.5%
239
 
1.5%
8
 
< 0.1%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rec_online_13
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3059965804
Minimum0
Maximum42
Zeros13874
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:02.409629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.109284926
Coefficient of variation (CV)3.625154649
Kurtosis215.9346435
Mean0.3059965804
Median Absolute Deviation (MAD)0
Skewness10.24371959
Sum5011
Variance1.230513046
MonotonicityNot monotonic
2022-03-22T19:37:02.473412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
013874
84.7%
11523
 
9.3%
2427
 
2.6%
3232
 
1.4%
4136
 
0.8%
560
 
0.4%
643
 
0.3%
727
 
0.2%
817
 
0.1%
910
 
0.1%
Other values (13)27
 
0.2%
ValueCountFrequency (%)
013874
84.7%
11523
 
9.3%
2427
 
2.6%
3232
 
1.4%
4136
 
0.8%
560
 
0.4%
643
 
0.3%
727
 
0.2%
817
 
0.1%
910
 
0.1%
ValueCountFrequency (%)
421
 
< 0.1%
281
 
< 0.1%
251
 
< 0.1%
221
 
< 0.1%
212
< 0.1%
172
< 0.1%
161
 
< 0.1%
152
< 0.1%
141
 
< 0.1%
133
< 0.1%

rec_online_50_b8
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03492916463
Minimum0
Maximum11
Zeros16052
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:02.539196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3155047349
Coefficient of variation (CV)9.03270199
Kurtosis315.7662459
Mean0.03492916463
Median Absolute Deviation (MAD)0
Skewness15.01410035
Sum572
Variance0.09954323773
MonotonicityNot monotonic
2022-03-22T19:37:02.598000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
016052
98.0%
1196
 
1.2%
278
 
0.5%
325
 
0.2%
59
 
0.1%
65
 
< 0.1%
45
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
016052
98.0%
1196
 
1.2%
278
 
0.5%
325
 
0.2%
45
 
< 0.1%
59
 
0.1%
65
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
101
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
65
 
< 0.1%
59
 
0.1%
45
 
< 0.1%
325
 
0.2%
278
 
0.5%
1196
1.2%

rec_online_30_b4
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1571812408
Minimum0
Maximum12
Zeros14848
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:02.656803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6331028876
Coefficient of variation (CV)4.027852715
Kurtosis63.12918737
Mean0.1571812408
Median Absolute Deviation (MAD)0
Skewness6.672620604
Sum2574
Variance0.4008192663
MonotonicityNot monotonic
2022-03-22T19:37:02.725570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
014848
90.7%
11002
 
6.1%
2284
 
1.7%
3124
 
0.8%
450
 
0.3%
526
 
0.2%
618
 
0.1%
712
 
0.1%
85
 
< 0.1%
93
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
014848
90.7%
11002
 
6.1%
2284
 
1.7%
3124
 
0.8%
450
 
0.3%
526
 
0.2%
618
 
0.1%
712
 
0.1%
85
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
85
 
< 0.1%
712
 
0.1%
618
 
0.1%
526
 
0.2%
450
0.3%
3124
0.8%

rec_online_40_b6
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04738641915
Minimum0
Maximum10
Zeros15906
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:02.790353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3333135185
Coefficient of variation (CV)7.033946107
Kurtosis147.1413963
Mean0.04738641915
Median Absolute Deviation (MAD)0
Skewness10.31114112
Sum776
Variance0.1110979016
MonotonicityNot monotonic
2022-03-22T19:37:02.854143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
015906
97.1%
1301
 
1.8%
285
 
0.5%
354
 
0.3%
418
 
0.1%
66
 
< 0.1%
55
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
015906
97.1%
1301
 
1.8%
285
 
0.5%
354
 
0.3%
418
 
0.1%
55
 
< 0.1%
66
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
66
 
< 0.1%
55
 
< 0.1%
418
 
0.1%
354
 
0.3%
285
 
0.5%
1301
 
1.8%
015906
97.1%

pct_rec_1190
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
16266 
 
103
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
16266
99.3%
103
 
0.6%
7
 
< 0.1%

Length

2022-03-22T19:37:02.926900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:02.973740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16266
99.3%
103
 
0.6%
7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pct_rec_690
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0122740596
Minimum0
Maximum6
Zeros16256
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:03.015600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1643925509
Coefficient of variation (CV)13.39349459
Kurtosis405.282445
Mean0.0122740596
Median Absolute Deviation (MAD)0
Skewness17.79860605
Sum201
Variance0.02702491078
MonotonicityNot monotonic
2022-03-22T19:37:03.072413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
016256
99.3%
166
 
0.4%
235
 
0.2%
315
 
0.1%
62
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
016256
99.3%
166
 
0.4%
235
 
0.2%
315
 
0.1%
42
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
42
 
< 0.1%
315
 
0.1%
235
 
0.2%
166
 
0.4%
016256
99.3%

rec_online_100_b18
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
16353 
 
18
 
3
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
16353
99.9%
18
 
0.1%
3
 
< 0.1%
2
 
< 0.1%

Length

2022-03-22T19:37:03.137197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:03.182047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16353
99.9%
18
 
0.1%
3
 
< 0.1%
2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pct_rec_sos_5
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002015144113
Minimum0
Maximum7
Zeros16359
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:03.220913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08005093807
Coefficient of variation (CV)39.72467157
Kurtosis4271.148337
Mean0.002015144113
Median Absolute Deviation (MAD)0
Skewness58.86004794
Sum33
Variance0.006408152687
MonotonicityNot monotonic
2022-03-22T19:37:03.278723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
016359
99.9%
110
 
0.1%
23
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
016359
99.9%
110
 
0.1%
23
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
41
 
< 0.1%
32
 
< 0.1%
23
 
< 0.1%
110
 
0.1%
016359
99.9%

sos_rec_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
16375 
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Common Values

ValueCountFrequency (%)
16375
> 99.9%
1
 
< 0.1%

Length

2022-03-22T19:37:03.345497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:03.385367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16375
> 99.9%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rec_online_8
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
16376 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
16376
100.0%

Length

2022-03-22T19:37:03.429220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:03.468090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16376
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

venda
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
11542 
4834 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
11542
70.5%
4834
29.5%

Length

2022-03-22T19:37:03.508951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:03.553800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
11542
70.5%
4834
29.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-22T19:36:59.367799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.306765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:48.350277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:49.301099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:50.595770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:51.524664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:52.484455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:53.423317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:54.376239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:55.279467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:56.521313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:57.512997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:58.439902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:59.439559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.391483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:48.427022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:49.377843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:50.669523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:51.598418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:52.556216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:53.495077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:54.446055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:55.666174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:56.595127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:57.581818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:58.510664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:59.512316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.471212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:48.500775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:49.456579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:50.743277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:51.672167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:52.628972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:53.566837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:54.515772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:55.735938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:56.671809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:57.652533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:58.581428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:59.590052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.552939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:48.575524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:49.533322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:50.818023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:51.747918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:52.700732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:53.644577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:54.584542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:55.806705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:56.747559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:57.729277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:58.652191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:59.665799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.631680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:48.646287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:49.608072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:50.883807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:51.819678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:52.771492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:53.715338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:54.651319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:55.875474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:56.821309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:57.800040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:58.718968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:59.745537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.717393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:48.725024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:49.687803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:50.958557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:51.896421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:52.846242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:53.795075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:54.724076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:55.949228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:56.900046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:57.874791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:58.794715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:59.824270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:36:47.798124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.